# Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Note that we don't combine the main with ray_trainer as ray_trainer is used by other main. """ import hydra import ray import torch from omegaconf import OmegaConf from split_monkey_patch import fit from verl import DataProto from verl.trainer.ppo.ray_trainer import RayPPOTrainer from verl.utils.reward_score import gsm8k, math_reward def _select_rm_score_fn(data_source): if data_source == "openai/gsm8k": return gsm8k.compute_score elif data_source == "lighteval/MATH": return math_reward.compute_score else: raise NotImplementedError class RewardManager: def __init__(self, tokenizer, num_examine) -> None: self.tokenizer = tokenizer self.num_examine = num_examine # the number of batches of decoded responses to print to the console def __call__(self, data: DataProto, return_dict: bool = False): """We will expand this function gradually based on the available datasets""" # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn if "rm_scores" in data.batch.keys(): return data.batch["rm_scores"] reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32) already_print_data_sources = {} for i in range(len(data)): data_item = data[i] # DataProtoItem prompt_ids = data_item.batch["prompts"] prompt_length = prompt_ids.shape[-1] valid_prompt_length = data_item.batch["attention_mask"][:prompt_length].sum() valid_prompt_ids = prompt_ids[-valid_prompt_length:] response_ids = data_item.batch["responses"] valid_response_length = data_item.batch["attention_mask"][prompt_length:].sum() valid_response_ids = response_ids[:valid_response_length] # decode sequences = torch.cat((valid_prompt_ids, valid_response_ids)) sequences_str = self.tokenizer.decode(sequences) ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] # select rm_score data_source = data_item.non_tensor_batch["data_source"] compute_score_fn = _select_rm_score_fn(data_source) score = compute_score_fn(solution_str=sequences_str, ground_truth=ground_truth) reward_tensor[i, valid_response_length - 1] = score if data_source not in already_print_data_sources: already_print_data_sources[data_source] = 0 if already_print_data_sources[data_source] < self.num_examine: already_print_data_sources[data_source] += 1 print(sequences_str) if return_dict: return {"reward_tensor": reward_tensor} else: return reward_tensor @hydra.main(config_path="config", config_name="ppo_trainer_split", version_base=None) def main(config): if not ray.is_initialized(): # this is for local ray cluster default_runtime_env = {"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN"}} ray_init_kwargs = config.ray_kwargs.get("ray_init", {}) runtime_env_kwargs = ray_init_kwargs.get("runtime_env", {}) runtime_env = OmegaConf.merge(default_runtime_env, runtime_env_kwargs) ray_init_kwargs = OmegaConf.create({**ray_init_kwargs, "runtime_env": runtime_env}) print(f"ray init kwargs: {ray_init_kwargs}") ray.init(**OmegaConf.to_container(ray_init_kwargs)) ray.get(main_task.remote(config)) @ray.remote def main_task(config): # print initial config from pprint import pprint from omegaconf import OmegaConf from verl.utils.fs import copy_to_local pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values OmegaConf.resolve(config) # download the checkpoint from hdfs local_path = copy_to_local(config.actor_rollout_ref.model.path) # instantiate tokenizer from verl.utils import hf_tokenizer tokenizer = hf_tokenizer(local_path) # define worker classes if config.actor_rollout_ref.actor.strategy in {"fsdp", "fsdp2"}: assert config.critic.strategy in {"fsdp", "fsdp2"} from verl.single_controller.ray import RayWorkerGroup from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker ray_worker_group_cls = RayWorkerGroup elif config.actor_rollout_ref.actor.strategy == "megatron": assert config.actor_rollout_ref.actor.strategy == config.critic.strategy from verl.single_controller.ray import RayWorkerGroup from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker ray_worker_group_cls = RayWorkerGroup else: raise NotImplementedError from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role role_worker_mapping = { Role.ActorRollout: ray.remote(ActorRolloutRefWorker), Role.Critic: ray.remote(CriticWorker), } # NOTE: initialze two resource pool actor_rollout_ref_pool_id = "actor_rollout_ref_pool" critic_pool_id = "critic_pool" if config.trainer.nnodes // 2 == 0 and config.trainer.n_gpus_per_node // 2 > 0: resource_pool_spec = { actor_rollout_ref_pool_id: [config.trainer.n_gpus_per_node // 2] * config.trainer.nnodes, critic_pool_id: [config.trainer.n_gpus_per_node // 2] * config.trainer.nnodes, } else: resource_pool_spec = { actor_rollout_ref_pool_id: [config.trainer.n_gpus_per_node] * (config.trainer.nnodes // 2), critic_pool_id: [config.trainer.n_gpus_per_node] * (config.trainer.nnodes // 2), } print(f"resource_pool_spec: {resource_pool_spec}") mapping = { Role.ActorRollout: actor_rollout_ref_pool_id, Role.Critic: critic_pool_id, } # use reference model if config.algorithm.use_kl_in_reward or config.actor_rollout_ref.actor.use_kl_loss: role_worker_mapping[Role.RefPolicy] = ray.remote(ActorRolloutRefWorker) mapping[Role.RefPolicy] = actor_rollout_ref_pool_id # we should adopt a multi-source reward function here # - for rule-based rm, we directly call a reward score # - for model-based rm, we call a model # - for code related prompt, we send to a sandbox if there are test cases # - finally, we combine all the rewards together # - The reward type depends on the tag of the data if config.reward_model.enable: if config.reward_model.strategy in {"fsdp", "fsdp2"}: from verl.workers.fsdp_workers import RewardModelWorker elif config.reward_model.strategy == "megatron": from verl.workers.megatron_workers import RewardModelWorker else: raise NotImplementedError role_worker_mapping[Role.RewardModel] = ray.remote(RewardModelWorker) mapping[Role.RewardModel] = critic_pool_id reward_fn = RewardManager(tokenizer=tokenizer, num_examine=0) # Note that we always use function-based RM for validation val_reward_fn = RewardManager(tokenizer=tokenizer, num_examine=1) resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) RayPPOTrainer.fit = fit trainer = RayPPOTrainer( config=config, tokenizer=tokenizer, role_worker_mapping=role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, reward_fn=reward_fn, val_reward_fn=val_reward_fn, ) trainer.init_workers() trainer.fit() if __name__ == "__main__": main()